While　face　verification　technology　is　proving　its　value　on　the　security　of　smartphones,　it　finds　a　more　suitable　environment　of　implementation　than　on　desktop　computers.　Targeting　to　the　＂close-range　frontal＂　photos　taken　by　the　front　camera　of　smartphones,　an　efficient　face　verification　approach　is　proposed　in　this　paper.　A　dedicated　rule-based　algorithm　is　first　implemented　to　detect　four　facial　feature　points　which　are　used　to　align　the　input　face　images　and　partition　the　face　region　into　four　components.　Based　on　each　group　of　facial　components　obtained　from　the　training　dataset,　an　eigen　subspace　is　constructed　through　principal　component　analysis(PCA).　Finally　the　weighted　sum　of　correlations　between　each　input　face　component　and　its　back-projection　onto　the　subspace　is　calculated　to　measure　the　similarity　of　the　input　person　against　that　in　the　dataset.　Experiments　are　conducted　on　a　dataset　with　464　face　images　taken　from　9　persons　with　variable　illumination,　background　and　expression.　The　Experimental　results　prove　a　98.2%　of　accuracy　on　feature　detection,　a　8.5%　of　EER　on　face　verification　and　the　computational　time　being　less　than　0.8　seconds　on　a　personal　computer.